Overview

Dataset statistics

Number of variables13
Number of observations2965
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.3 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qt_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with qt_invoicesHigh correlation
qt_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_assortmentHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_assortment is highly correlated with assortment and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qt_invoices and 1 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
assortment is highly correlated with qt_invoicesHigh correlation
avg_ticket is highly correlated with qt_returned and 1 other fieldsHigh correlation
qt_returned is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
gross_revenue is highly correlated with qt_invoices and 2 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with no_itemsHigh correlation
gross_revenue is highly correlated with qt_invoices and 3 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with qt_returned and 1 other fieldsHigh correlation
qt_returned is highly correlated with no_items and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_assortment is highly correlated with avg_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 25.14435057) Skewed
qt_returned is highly skewed (γ1 = 26.83769591) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qt_returned has 1480 (49.9%) zeros Zeros

Reproduction

Analysis started2021-11-18 15:55:21.443632
Analysis finished2021-11-18 15:55:55.539562
Duration34.1 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2965
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2308.542664
Minimum0
Maximum5689
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:55.665990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.2
Q1926
median2113
Q33523
95-th percentile5010.2
Maximum5689
Range5689
Interquartile range (IQR)2597

Descriptive statistics

Standard deviation1547.283204
Coefficient of variation (CV)0.670242412
Kurtosis-1.011794798
Mean2308.542664
Median Absolute Deviation (MAD)1265
Skewness0.3400689729
Sum6844829
Variance2394085.312
MonotonicityStrictly increasing
2021-11-18T15:55:55.854808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30021
 
< 0.1%
29871
 
< 0.1%
29901
 
< 0.1%
29911
 
< 0.1%
29921
 
< 0.1%
29931
 
< 0.1%
29961
 
< 0.1%
29981
 
< 0.1%
29991
 
< 0.1%
Other values (2955)2955
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56891
< 0.1%
56701
< 0.1%
56601
< 0.1%
56541
< 0.1%
56331
< 0.1%
56291
< 0.1%
56231
< 0.1%
56121
< 0.1%
56111
< 0.1%
56011
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2965
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.24992
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:56.022203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.2
Q113799
median15220
Q316770
95-th percentile17964.8
Maximum18287
Range5940
Interquartile range (IQR)2971

Descriptive statistics

Standard deviation1719.522705
Coefficient of variation (CV)0.1126060617
Kurtosis-1.206368645
Mean15270.24992
Median Absolute Deviation (MAD)1489
Skewness0.03249797116
Sum45276291
Variance2956758.332
MonotonicityNot monotonic
2021-11-18T15:55:56.218864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2955)2955
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2950
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.612793
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:56.435461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.892
Q1570.96
median1084.1
Q32308.06
95-th percentile7180.164
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10118.95249
Coefficient of variation (CV)3.756647028
Kurtosis398.8579858
Mean2693.612793
Median Absolute Deviation (MAD)670.06
Skewness17.65630412
Sum7986561.93
Variance102393199.5
MonotonicityNot monotonic
2021-11-18T15:55:56.638978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.022
 
0.1%
1353.742
 
0.1%
734.942
 
0.1%
1025.442
 
0.1%
598.22
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
2092.322
 
0.1%
379.652
 
0.1%
745.062
 
0.1%
Other values (2940)2945
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
136275.721
< 0.1%
124564.531
< 0.1%
116729.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.21450253
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:56.825091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.57878668
Coefficient of variation (CV)1.208119406
Kurtosis2.760237102
Mean64.21450253
Median Absolute Deviation (MAD)26
Skewness1.794679718
Sum190396
Variance6018.468143
MonotonicityNot monotonic
2021-11-18T15:55:57.007876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
385
 
2.9%
284
 
2.8%
876
 
2.6%
1067
 
2.3%
766
 
2.2%
966
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2216
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
284
2.8%
385
2.9%
487
2.9%
543
1.5%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3723
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qt_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724451939
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:57.211423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.847025721
Coefficient of variation (CV)1.545479954
Kurtosis190.178526
Mean5.724451939
Median Absolute Deviation (MAD)2
Skewness10.74593132
Sum16973
Variance78.26986411
MonotonicityNot monotonic
2021-11-18T15:55:57.406723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2784
26.4%
3497
16.8%
4394
13.3%
5236
 
8.0%
1189
 
6.4%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
Other values (47)332
11.2%
ValueCountFrequency (%)
1189
 
6.4%
2784
26.4%
3497
16.8%
4394
13.3%
5236
 
8.0%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
ValueCountFrequency (%)
2061
< 0.1%
1981
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%

no_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1676
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1583.442833
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:57.867539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median642
Q31401
95-th percentile4404
Maximum196844
Range196842
Interquartile range (IQR)1104

Descriptive statistics

Standard deviation5707.571653
Coefficient of variation (CV)3.604532815
Kurtosis516.3654066
Mean1583.442833
Median Absolute Deviation (MAD)422
Skewness18.73041713
Sum4694908
Variance32576374.17
MonotonicityNot monotonic
2021-11-18T15:55:58.070183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2888
 
0.3%
2728
 
0.3%
2608
 
0.3%
2468
 
0.3%
1348
 
0.3%
848
 
0.3%
2197
 
0.2%
Other values (1666)2881
97.2%
ValueCountFrequency (%)
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
261
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
801791
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578721
< 0.1%
502551
< 0.1%

assortment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.810118
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:58.260213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.4468531
Coefficient of variation (CV)2.19401184
Kurtosis354.1084542
Mean122.810118
Median Absolute Deviation (MAD)44
Skewness15.67328761
Sum364132
Variance72601.60665
MonotonicityNot monotonic
2021-11-18T15:55:58.438358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2844
 
1.5%
2037
 
1.2%
3535
 
1.2%
2934
 
1.1%
1533
 
1.1%
1933
 
1.1%
1132
 
1.1%
2531
 
1.0%
2630
 
1.0%
2730
 
1.0%
Other values (457)2626
88.6%
ValueCountFrequency (%)
15
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
628
0.9%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
55891
< 0.1%
50951
< 0.1%
45801
< 0.1%
26971
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16721
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1992
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.0066172
Minimum2.15
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:58.635609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.914
Q113.12
median17.94
Q324.98
95-th percentile90.236
Maximum4453.43
Range4451.28
Interquartile range (IQR)11.86

Descriptive statistics

Standard deviation119.5919146
Coefficient of variation (CV)3.623270869
Kurtosis812.1480741
Mean33.0066172
Median Absolute Deviation (MAD)5.97
Skewness25.14435057
Sum97864.62
Variance14302.22603
MonotonicityNot monotonic
2021-11-18T15:55:58.802328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.497
 
0.2%
16.826
 
0.2%
16.396
 
0.2%
16.926
 
0.2%
19.066
 
0.2%
17.666
 
0.2%
17.716
 
0.2%
19.445
 
0.2%
105
 
0.2%
17.135
 
0.2%
Other values (1982)2907
98.0%
ValueCountFrequency (%)
2.151
< 0.1%
2.431
< 0.1%
2.461
< 0.1%
2.511
< 0.1%
2.521
< 0.1%
2.651
< 0.1%
2.661
< 0.1%
2.711
< 0.1%
2.761
< 0.1%
2.771
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.991
< 0.1%
872.131
< 0.1%
841.021
< 0.1%
651.171
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.33252032
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:58.980610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.28571429
Q385.33333333
95-th percentile200.8
Maximum366
Range365
Interquartile range (IQR)59.33333333

Descriptive statistics

Standard deviation63.52158276
Coefficient of variation (CV)0.943401234
Kurtosis4.903585736
Mean67.33252032
Median Absolute Deviation (MAD)26.28571429
Skewness2.065604551
Sum199640.9227
Variance4034.991476
MonotonicityNot monotonic
2021-11-18T15:55:59.158753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
7021
 
0.7%
421
 
0.7%
720
 
0.7%
3518
 
0.6%
4918
 
0.6%
4617
 
0.6%
1117
 
0.6%
2117
 
0.6%
516
 
0.5%
Other values (1248)2775
93.6%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
421
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1347
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06318195167
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:59.343574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.0293040293
Q30.05539358601
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03761580823

Descriptive statistics

Standard deviation0.1344202854
Coefficient of variation (CV)2.127510813
Kurtosis121.9648159
Mean0.06318195167
Median Absolute Deviation (MAD)0.01426643532
Skewness8.793597948
Sum187.3344867
Variance0.01806881312
MonotonicityNot monotonic
2021-11-18T15:55:59.531154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.333333333321
 
0.7%
0.166666666721
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.0357142857115
 
0.5%
0.415
 
0.5%
0.2515
 
0.5%
0.0238095238115
 
0.5%
Other values (1337)2791
94.1%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%
0.83333333331
 
< 0.1%
0.751
 
< 0.1%
0.666666666712
0.4%
0.64879356571
 
< 0.1%
0.61
 
< 0.1%

qt_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct172
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.95615514
Minimum0
Maximum9014
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:55:59.712357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile61.8
Maximum9014
Range9014
Interquartile range (IQR)6

Descriptive statistics

Standard deviation228.7536437
Coefficient of variation (CV)9.166221416
Kurtosis911.4384425
Mean24.95615514
Median Absolute Deviation (MAD)1
Skewness26.83769591
Sum73995
Variance52328.22952
MonotonicityNot monotonic
2021-11-18T15:55:59.882718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
49.9%
1295
 
9.9%
3169
 
5.7%
693
 
3.1%
286
 
2.9%
471
 
2.4%
543
 
1.5%
1243
 
1.5%
840
 
1.3%
738
 
1.3%
Other values (162)607
20.5%
ValueCountFrequency (%)
01480
49.9%
1295
 
9.9%
286
 
2.9%
3169
 
5.7%
471
 
2.4%
543
 
1.5%
693
 
3.1%
738
 
1.3%
840
 
1.3%
936
 
1.2%
ValueCountFrequency (%)
90141
< 0.1%
48241
< 0.1%
40271
< 0.1%
23022
0.1%
17761
< 0.1%
16081
< 0.1%
15891
< 0.1%
15151
< 0.1%
12781
< 0.1%
12421
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.5105663
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:56:00.047637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.27777778
Q1103.3
median172.3333333
Q3281.6923077
95-th percentile599.76
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.3923077

Descriptive statistics

Standard deviation284.1165832
Coefficient of variation (CV)1.201284947
Kurtosis102.5495958
Mean236.5105663
Median Absolute Deviation (MAD)83
Skewness7.694246699
Sum701253.8291
Variance80722.23286
MonotonicityNot monotonic
2021-11-18T15:56:00.212512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
739
 
0.3%
869
 
0.3%
608
 
0.3%
1408
 
0.3%
1638
 
0.3%
1368
 
0.3%
758
 
0.3%
Other values (1969)2877
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_assortment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct910
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.50105306
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-18T15:56:00.374984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.709677419
median13.6
Q322
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.29032258

Descriptive statistics

Standard deviation15.46481281
Coefficient of variation (CV)0.883650416
Kurtosis29.30907341
Mean17.50105306
Median Absolute Deviation (MAD)6.6
Skewness3.436201515
Sum51890.62232
Variance239.1604354
MonotonicityNot monotonic
2021-11-18T15:56:00.578807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1343
 
1.5%
942
 
1.4%
839
 
1.3%
1639
 
1.3%
1438
 
1.3%
1738
 
1.3%
737
 
1.2%
536
 
1.2%
1136
 
1.2%
1534
 
1.1%
Other values (900)2583
87.1%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2021-11-18T15:55:52.851829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.003051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.083558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.614747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.795098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.256121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.430563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.769572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.201960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.371203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.457169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.518860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.759567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.010289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.210137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.273414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.789358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.971937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.412352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.618993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.911387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.369815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.549841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.615012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.679873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.915952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.162244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.376528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.434651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.956678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:33.170931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.564802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.795924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:40.101658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.500084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.718815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.757375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.832463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.085976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.312424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.520857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.577030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.121252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:33.356812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.739372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.993438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:40.307993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.638821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.862263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.889666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.199009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.224403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.472624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.664667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.723500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.294163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:33.569858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.911565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:38.152254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:40.478132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.811405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.025744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.058842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.351042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.391495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.638189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.810831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:26.877583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.452767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:33.745340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.072265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:38.293164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:40.657393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.951327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.171079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.214943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.500270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.548629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.801610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:24.969152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:29.481730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.640963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:33.937631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.254983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:38.460763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:40.811264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:43.148749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.337226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.379555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.670961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.728721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:53.965694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.140357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:29.644392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.828549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:34.103503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.402894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:38.689266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:41.011753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:43.355449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.491311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.547337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.837747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:51.906722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:54.121448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.285623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:29.787338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:31.993463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:34.264142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.575787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:38.857491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:41.185697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:43.509514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.645886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.694687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:49.981205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:52.055981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:54.281629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.440538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:29.960615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.153372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:34.420272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.772026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.065786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:41.550925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:43.680276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.812877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:47.854139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.148313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:52.222304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:54.445070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.609662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.130849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.314390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:34.595013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:36.928734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.240074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:41.710264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:43.851912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:45.988250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.034739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.312155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:52.372406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:54.610413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.749148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.287521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.457135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:34.904891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.100761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.435275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:41.877789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.014209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.138564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.196152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.451752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:52.520155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:54.761750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:25.917773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:30.447728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:32.632621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:35.070384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:37.266119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:39.581394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:42.038804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:44.197418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:46.305415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:48.362549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:50.603174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-18T15:55:52.691134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-18T15:56:00.728092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-18T15:56:00.949559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-18T15:56:01.168807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-18T15:56:01.382191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-18T15:55:55.059130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-18T15:55:55.394814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqt_invoicesno_itemsassortmentavg_ticketavg_recency_daysfrequencyqt_returnedavg_basket_sizeavg_assortment
00178505391.21372.034.01733.0297.018.1535.5000000.48611121.050.9705880.617647
11130473232.5956.09.01390.0171.018.9027.2500000.0487806.0154.44444411.666667
22125836705.382.015.05028.0232.028.9023.1875000.04569950.0335.2000007.600000
3313748948.2595.05.0439.028.033.8792.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.008.6000000.13636422.026.6666670.333333
55152914623.3025.014.02102.0102.045.3323.2000000.05444127.0150.1428574.357143
66146885630.877.021.03621.0327.017.2218.3000000.073569281.0172.4285717.047619
77178095411.9116.012.02057.061.088.7235.7000000.03910641.0171.4166673.833333
881531160767.900.091.038194.02379.025.544.1444440.315508231.0419.7142866.230769
99160982005.6387.07.0613.067.029.9347.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqt_invoicesno_itemsassortmentavg_ticketavg_recency_daysfrequencyqt_returnedavg_basket_sizeavg_assortment
29555601177271060.2515.01.0645.066.016.066.00.2857146.0645.00000066.000000
2956561117232421.522.02.0203.036.011.7112.00.1538460.0101.50000015.000000
2957561217468137.0010.02.0116.05.027.404.00.4000000.058.0000002.500000
2958562313596697.045.02.0406.0166.04.207.00.2500000.0203.00000066.500000
29595629148931237.859.02.0799.073.016.962.00.6666670.0399.50000036.000000
2960563312479473.2011.01.0382.030.015.774.00.33333334.0382.00000030.000000
2961565414126706.137.03.0508.015.047.083.01.00000050.0169.3333334.666667
29625660135211092.391.03.0733.0435.02.514.50.3000000.0244.333333104.000000
2963567015060301.848.04.0262.0120.02.521.02.0000000.065.50000020.000000
2964568912558269.967.01.0196.011.024.546.00.285714102.0196.00000011.000000